import gradio as gr
import torch
from PIL import Image, ImageDraw
from transformers import AutoProcessor
from modeling_florence2 import Florence2ForConditionalGeneration
import io
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import numpy as np
import random

device = "cuda" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32

model = Florence2ForConditionalGeneration.from_pretrained("PleIAs/Florence-PDF", torch_dtype=torch_dtype, trust_remote_code=True).to(device)
processor = AutoProcessor.from_pretrained("PleIAs/Florence-PDF", trust_remote_code=True)

TASK_PROMPTS = {
    "Caption": "<CAPTION>",
    "Detailed Caption": "<DETAILED_CAPTION>",
    "More Detailed Caption": "<MORE_DETAILED_CAPTION>",
    "Object Detection": "<OD>",
    "Dense Region Caption": "<DENSE_REGION_CAPTION>",
    "OCR": "<OCR>",
    "OCR with Region": "<OCR_WITH_REGION>",
    "Region Proposal": "<REGION_PROPOSAL>"
}

IMAGE_TASKS = ["Object Detection", "Dense Region Caption", "Region Proposal", "OCR with Region"]
TEXT_TASKS = ["Caption", "Detailed Caption", "More Detailed Caption", "OCR"]

colormap = ['blue','orange','green','purple','brown','pink','gray','olive','cyan','red',
            'lime','indigo','violet','aqua','magenta','coral','gold','tan','skyblue']

def fig_to_pil(fig):
    buf = io.BytesIO()
    fig.savefig(buf, format='png')
    buf.seek(0)
    return Image.open(buf)

def plot_bbox(image, data):
    fig, ax = plt.subplots()
    ax.imshow(image)
    for bbox, label in zip(data['bboxes'], data['labels']):
        x1, y1, x2, y2 = bbox
        rect = patches.Rectangle((x1, y1), x2-x1, y2-y1, linewidth=1, edgecolor='r', facecolor='none')
        ax.add_patch(rect)
        plt.text(x1, y1, label, color='white', fontsize=8, bbox=dict(facecolor='red', alpha=0.5))
    ax.axis('off')
    return fig

def draw_ocr_bboxes(image, prediction):
    scale = 1
    draw = ImageDraw.Draw(image)
    bboxes, labels = prediction['quad_boxes'], prediction['labels']
    for box, label in zip(bboxes, labels):
        color = random.choice(colormap)
        new_box = (np.array(box) * scale).tolist()
        draw.polygon(new_box, width=3, outline=color)
        draw.text((new_box[0]+8, new_box[1]+2),
                  "{}".format(label),
                  align="right",
                  fill=color)
    return image

def process_image(image, task):
    prompt = TASK_PROMPTS[task]
    inputs = processor(text=prompt, images=image, return_tensors="pt").to(device, torch_dtype)
    
    generated_ids = model.generate(
        **inputs,
        max_new_tokens=1024,
        num_beams=3,
        do_sample=False
    )
    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
    
    parsed_answer = processor.post_process_generation(generated_text, task=prompt, image_size=(image.width, image.height))
    
    return parsed_answer

def main_process(image, task):
    result = process_image(image, task)
    
    if task in IMAGE_TASKS:
        if task == "OCR with Region":
            output_image = draw_ocr_bboxes(image.copy(), result[TASK_PROMPTS[task]])
        else:
            fig = plot_bbox(image, result[TASK_PROMPTS[task]])
            output_image = fig_to_pil(fig)
        return output_image, gr.update(visible=True), None, gr.update(visible=False)
    else:
        return None, gr.update(visible=False), str(result), gr.update(visible=True)

def reset_outputs():
    return None, gr.update(visible=False), None, gr.update(visible=True)

with gr.Blocks(title="Florence-2 Demo") as iface:
    gr.Markdown("# Florence-2 Demo")
    gr.Markdown("Upload an image and select a task to process with Florence-2.")
    
    with gr.Column():
        image_input = gr.Image(type="pil", label="Input Image")
        task_dropdown = gr.Dropdown(list(TASK_PROMPTS.keys()), label="Task", value="Caption")
    
    with gr.Row():
        submit_button = gr.Button("Process")
        reset_button = gr.Button("Reset")
    
    output_image = gr.Image(label="Processed Image", visible=False)
    output_text = gr.Textbox(label="Output", visible=True)
    
    def process_and_update(image, task):
        if image is None:
            return None, gr.update(visible=False), "Please upload an image first.", gr.update(visible=True)
        return main_process(image, task)
    
    submit_button.click(
        fn=process_and_update,
        inputs=[image_input, task_dropdown],
        outputs=[output_image, output_image, output_text, output_text]
    )
    
    reset_button.click(
        fn=reset_outputs,
        inputs=[],
        outputs=[output_image, output_image, output_text, output_text]
    )
    
    task_dropdown.change(
        fn=lambda task: (gr.update(visible=task in IMAGE_TASKS), gr.update(visible=task in TEXT_TASKS)),
        inputs=[task_dropdown],
        outputs=[output_image, output_text]
    )

iface.launch()